from __future__ import annotations

from itertools import chain
from typing import TYPE_CHECKING, Any, Callable

from polars import functions as F
from polars._utils.convert import parse_as_duration_string
from polars._utils.deprecation import deprecated
from polars._utils.parse.expr import _parse_inputs_as_iterable

if TYPE_CHECKING:
    import sys
    from collections.abc import Iterable
    from datetime import timedelta

    from polars import DataFrame
    from polars._typing import (
        ClosedInterval,
        IntoExpr,
        Label,
        QuantileMethod,
        SchemaDict,
        StartBy,
    )
    from polars.lazyframe.group_by import LazyGroupBy

    if sys.version_info >= (3, 11):
        from typing import Self
    else:
        from typing_extensions import Self

    if sys.version_info >= (3, 13):
        from warnings import deprecated
    else:
        from typing_extensions import deprecated  # noqa: TC004


class GroupBy:
    """Starts a new GroupBy operation."""

    def __init__(
        self,
        df: DataFrame,
        *by: IntoExpr | Iterable[IntoExpr],
        maintain_order: bool,
        predicates: Iterable[Any] | None,
        **named_by: IntoExpr,
    ) -> None:
        """
        Utility class for performing a group by operation over the given DataFrame.

        Generated by calling `df.group_by(...)`.

        Parameters
        ----------
        df
            DataFrame to perform the group by operation over.
        *by
            Column or columns to group by. Accepts expression input. Strings are parsed
            as column names.
        maintain_order
            Ensure that the order of the groups is consistent with the input data.
            This is slower than a default group by.
        predicates
            Predicate expressions to filter groups after aggregation.
        **named_by
            Additional column(s) to group by, specified as keyword arguments.
            The columns will be named as the keyword used.
        """
        self.df = df
        self.by = by
        self.named_by = named_by
        self.maintain_order = maintain_order
        self.predicates = predicates

    def _lgb(self) -> LazyGroupBy:
        group_by = self.df.lazy().group_by(
            *self.by, **self.named_by, maintain_order=self.maintain_order
        )
        if self.predicates:
            return group_by.having(self.predicates)
        return group_by

    def __iter__(self) -> Self:
        """
        Allows iteration over the groups of the group by operation.

        Each group is represented by a tuple of `(name, data)`. The group names are
        tuples of the distinct group values that identify each group.

        Examples
        --------
        >>> df = pl.DataFrame({"foo": ["a", "a", "b"], "bar": [1, 2, 3]})
        >>> for name, data in df.group_by("foo"):  # doctest: +SKIP
        ...     print(name)
        ...     print(data)
        (a,)
        shape: (2, 2)
        ┌─────┬─────┐
        │ foo ┆ bar │
        │ --- ┆ --- │
        │ str ┆ i64 │
        ╞═════╪═════╡
        │ a   ┆ 1   │
        │ a   ┆ 2   │
        └─────┴─────┘
        (b,)
        shape: (1, 2)
        ┌─────┬─────┐
        │ foo ┆ bar │
        │ --- ┆ --- │
        │ str ┆ i64 │
        ╞═════╪═════╡
        │ b   ┆ 3   │
        └─────┴─────┘
        """
        # Every group gather can trigger a rechunk, so do early.
        from polars.lazyframe.opt_flags import QueryOptFlags

        self.df = self.df.rechunk()
        temp_col = "__POLARS_GB_GROUP_INDICES"
        groups_df = (
            self.df.lazy()
            .with_row_index("__POLARS_GB_ROW_INDEX")
            .group_by(*self.by, **self.named_by, maintain_order=self.maintain_order)
            .agg(F.first().alias(temp_col))
            .collect(optimizations=QueryOptFlags.none())
        )

        self._group_names = groups_df.select(F.all().exclude(temp_col)).iter_rows()
        self._group_indices = groups_df.select(temp_col).to_series()
        self._current_index = 0

        return self

    def __next__(self) -> tuple[tuple[Any, ...], DataFrame]:
        if self._current_index >= len(self._group_indices):
            raise StopIteration

        group_name = next(self._group_names)
        group_data = self.df[self._group_indices[self._current_index], :]
        self._current_index += 1

        return group_name, group_data

    def having(self, *predicates: IntoExpr | Iterable[IntoExpr]) -> GroupBy:
        """
        Filter groups with a list of predicates after aggregation.

        Using this method is equivalent to adding the predicates to the aggregation and
        filtering afterwards.

        This method can be chained and all conditions will be combined using `&`.

        Parameters
        ----------
        *predicates
            Expressions that evaluate to a boolean value for each group. Typically, this
            requires the use of an aggregation function. Multiple predicates are
            combined using `&`.

        Examples
        --------
        Only keep groups that contain more than one element.

        >>> df = pl.DataFrame(
        ...     {
        ...         "a": ["a", "b", "a", "b", "c"],
        ...     }
        ... )
        >>> df.group_by("a").having(pl.len() > 1).agg()  # doctest: +IGNORE_RESULT
        shape: (2, 1)
        ┌─────┐
        │ a   │
        │ --- │
        │ str │
        ╞═════╡
        │ b   │
        │ a   │
        └─────┘
        """
        return GroupBy(
            self.df,
            *self.by,
            maintain_order=self.maintain_order,
            predicates=_chain_predicates(self.predicates, predicates),
            **self.named_by,
        )

    def agg(
        self,
        *aggs: IntoExpr | Iterable[IntoExpr],
        **named_aggs: IntoExpr,
    ) -> DataFrame:
        """
        Compute aggregations for each group of a group by operation.

        Parameters
        ----------
        *aggs
            Aggregations to compute for each group of the group by operation,
            specified as positional arguments.
            Accepts expression input. Strings are parsed as column names.
        **named_aggs
            Additional aggregations, specified as keyword arguments.
            The resulting columns will be renamed to the keyword used.

        Examples
        --------
        Compute the aggregation of the columns for each group.

        >>> df = pl.DataFrame(
        ...     {
        ...         "a": ["a", "b", "a", "b", "c"],
        ...         "b": [1, 2, 1, 3, 3],
        ...         "c": [5, 4, 3, 2, 1],
        ...     }
        ... )
        >>> df.group_by("a").agg(pl.col("b"), pl.col("c"))  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬───────────┬───────────┐
        │ a   ┆ b         ┆ c         │
        │ --- ┆ ---       ┆ ---       │
        │ str ┆ list[i64] ┆ list[i64] │
        ╞═════╪═══════════╪═══════════╡
        │ a   ┆ [1, 1]    ┆ [5, 3]    │
        │ b   ┆ [2, 3]    ┆ [4, 2]    │
        │ c   ┆ [3]       ┆ [1]       │
        └─────┴───────────┴───────────┘

        Compute the sum of a column for each group.

        >>> df.group_by("a").agg(pl.col("b").sum())  # doctest: +IGNORE_RESULT
        shape: (3, 2)
        ┌─────┬─────┐
        │ a   ┆ b   │
        │ --- ┆ --- │
        │ str ┆ i64 │
        ╞═════╪═════╡
        │ a   ┆ 2   │
        │ b   ┆ 5   │
        │ c   ┆ 3   │
        └─────┴─────┘

        Compute multiple aggregates at once by passing a list of expressions.

        >>> df.group_by("a").agg([pl.sum("b"), pl.mean("c")])  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬─────┬─────┐
        │ a   ┆ b   ┆ c   │
        │ --- ┆ --- ┆ --- │
        │ str ┆ i64 ┆ f64 │
        ╞═════╪═════╪═════╡
        │ c   ┆ 3   ┆ 1.0 │
        │ a   ┆ 2   ┆ 4.0 │
        │ b   ┆ 5   ┆ 3.0 │
        └─────┴─────┴─────┘

        Or use positional arguments to compute multiple aggregations in the same way.

        >>> df.group_by("a").agg(
        ...     pl.sum("b").name.suffix("_sum"),
        ...     (pl.col("c") ** 2).mean().name.suffix("_mean_squared"),
        ... )  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬───────┬────────────────┐
        │ a   ┆ b_sum ┆ c_mean_squared │
        │ --- ┆ ---   ┆ ---            │
        │ str ┆ i64   ┆ f64            │
        ╞═════╪═══════╪════════════════╡
        │ a   ┆ 2     ┆ 17.0           │
        │ c   ┆ 3     ┆ 1.0            │
        │ b   ┆ 5     ┆ 10.0           │
        └─────┴───────┴────────────────┘

        Use keyword arguments to easily name your expression inputs.

        >>> df.group_by("a").agg(
        ...     b_sum=pl.sum("b"),
        ...     c_mean_squared=(pl.col("c") ** 2).mean(),
        ... )  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬───────┬────────────────┐
        │ a   ┆ b_sum ┆ c_mean_squared │
        │ --- ┆ ---   ┆ ---            │
        │ str ┆ i64   ┆ f64            │
        ╞═════╪═══════╪════════════════╡
        │ a   ┆ 2     ┆ 17.0           │
        │ c   ┆ 3     ┆ 1.0            │
        │ b   ┆ 5     ┆ 10.0           │
        └─────┴───────┴────────────────┘
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self._lgb()
            .agg(*aggs, **named_aggs)
            .collect(optimizations=QueryOptFlags.none())
        )

    def map_groups(self, function: Callable[[DataFrame], DataFrame]) -> DataFrame:
        """
        Apply a custom/user-defined function (UDF) over the groups as a sub-DataFrame.

        .. warning::
            This method is much slower than the native expressions API.
            Only use it if you cannot implement your logic otherwise.

        Implementing logic using a Python function is almost always *significantly*
        slower and more memory intensive than implementing the same logic using
        the native expression API because:

        - The native expression engine runs in Rust; UDFs run in Python.
        - Use of Python UDFs forces the DataFrame to be materialized in memory.
        - Polars-native expressions can be parallelised (UDFs cannot).
        - Polars-native expressions can be logically optimised (UDFs cannot).

        Wherever possible you should strongly prefer the native expression API
        to achieve the best performance.

        Parameters
        ----------
        function
            Custom function that receives a DataFrame and returns a DataFrame.

        Returns
        -------
        DataFrame

        Examples
        --------
        For each color group sample two rows:

        >>> df = pl.DataFrame(
        ...     {
        ...         "id": [0, 1, 2, 3, 4],
        ...         "color": ["red", "green", "green", "red", "red"],
        ...         "shape": ["square", "triangle", "square", "triangle", "square"],
        ...     }
        ... )
        >>> df.group_by("color").map_groups(
        ...     lambda group_df: group_df.sample(2)
        ... )  # doctest: +IGNORE_RESULT
        shape: (4, 3)
        ┌─────┬───────┬──────────┐
        │ id  ┆ color ┆ shape    │
        │ --- ┆ ---   ┆ ---      │
        │ i64 ┆ str   ┆ str      │
        ╞═════╪═══════╪══════════╡
        │ 1   ┆ green ┆ triangle │
        │ 2   ┆ green ┆ square   │
        │ 4   ┆ red   ┆ square   │
        │ 3   ┆ red   ┆ triangle │
        └─────┴───────┴──────────┘

        It is better to implement this with an expression:

        >>> df.filter(
        ...     pl.int_range(pl.len()).shuffle().over("color") < 2
        ... )  # doctest: +IGNORE_RESULT
        """
        if self.predicates:
            msg = "cannot call `map_groups` when filtering groups with `having`"
            raise TypeError(msg)
        if self.named_by:
            msg = "cannot call `map_groups` when grouping by named expressions"
            raise TypeError(msg)
        if not all(isinstance(c, str) for c in self.by):
            msg = "cannot call `map_groups` when grouping by an expression"
            raise TypeError(msg)

        by_strs: list[str] = self.by  # type: ignore[assignment]

        return self.df.__class__._from_pydf(
            self.df._df.group_by_map_groups(by_strs, function, self.maintain_order)
        )

    def head(self, n: int = 5) -> DataFrame:
        """
        Get the first `n` rows of each group.

        Parameters
        ----------
        n
            Number of rows to return.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "letters": ["c", "c", "a", "c", "a", "b"],
        ...         "nrs": [1, 2, 3, 4, 5, 6],
        ...     }
        ... )
        >>> df
        shape: (6, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ c       ┆ 1   │
        │ c       ┆ 2   │
        │ a       ┆ 3   │
        │ c       ┆ 4   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        └─────────┴─────┘
        >>> df.group_by("letters").head(2).sort("letters")
        shape: (5, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ a       ┆ 3   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        │ c       ┆ 1   │
        │ c       ┆ 2   │
        └─────────┴─────┘
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return self._lgb().head(n).collect(optimizations=QueryOptFlags._eager())

    def tail(self, n: int = 5) -> DataFrame:
        """
        Get the last `n` rows of each group.

        Parameters
        ----------
        n
            Number of rows to return.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "letters": ["c", "c", "a", "c", "a", "b"],
        ...         "nrs": [1, 2, 3, 4, 5, 6],
        ...     }
        ... )
        >>> df
        shape: (6, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ c       ┆ 1   │
        │ c       ┆ 2   │
        │ a       ┆ 3   │
        │ c       ┆ 4   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        └─────────┴─────┘
        >>> df.group_by("letters").tail(2).sort("letters")
        shape: (5, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ a       ┆ 3   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        │ c       ┆ 2   │
        │ c       ┆ 4   │
        └─────────┴─────┘
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return self._lgb().tail(n).collect(optimizations=QueryOptFlags.none())

    def all(self) -> DataFrame:
        """
        Aggregate the groups into Series.

        Examples
        --------
        >>> df = pl.DataFrame({"a": ["one", "two", "one", "two"], "b": [1, 2, 3, 4]})
        >>> df.group_by("a", maintain_order=True).all()
        shape: (2, 2)
        ┌─────┬───────────┐
        │ a   ┆ b         │
        │ --- ┆ ---       │
        │ str ┆ list[i64] │
        ╞═════╪═══════════╡
        │ one ┆ [1, 3]    │
        │ two ┆ [2, 4]    │
        └─────┴───────────┘
        """
        return self.agg(F.all())

    def len(self, name: str | None = None) -> DataFrame:
        """
        Return the number of rows in each group.

        Parameters
        ----------
        name
            Assign a name to the resulting column; if unset, defaults to "len".

        Examples
        --------
        >>> df = pl.DataFrame({"a": ["Apple", "Apple", "Orange"], "b": [1, None, 2]})
        >>> df.group_by("a").len()  # doctest: +IGNORE_RESULT
        shape: (2, 2)
        ┌────────┬─────┐
        │ a      ┆ len │
        │ ---    ┆ --- │
        │ str    ┆ u32 │
        ╞════════╪═════╡
        │ Apple  ┆ 2   │
        │ Orange ┆ 1   │
        └────────┴─────┘
        >>> df.group_by("a").len(name="n")  # doctest: +IGNORE_RESULT
        shape: (2, 2)
        ┌────────┬─────┐
        │ a      ┆ n   │
        │ ---    ┆ --- │
        │ str    ┆ u32 │
        ╞════════╪═════╡
        │ Apple  ┆ 2   │
        │ Orange ┆ 1   │
        └────────┴─────┘
        """
        len_expr = F.len()
        if name is not None:
            len_expr = len_expr.alias(name)
        return self.agg(len_expr)

    @deprecated("`GroupBy.count` was renamed; use `GroupBy.len` instead")
    def count(self) -> DataFrame:
        """
        Return the number of rows in each group.

        .. deprecated:: 0.20.5
            This method has been renamed to :func:`GroupBy.len`.

        Rows containing null values count towards the total.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": ["Apple", "Apple", "Orange"],
        ...         "b": [1, None, 2],
        ...     }
        ... )
        >>> df.group_by("a").count()  # doctest: +SKIP
        shape: (2, 2)
        ┌────────┬───────┐
        │ a      ┆ count │
        │ ---    ┆ ---   │
        │ str    ┆ u32   │
        ╞════════╪═══════╡
        │ Apple  ┆ 2     │
        │ Orange ┆ 1     │
        └────────┴───────┘
        """
        return self.agg(F.len().alias("count"))

    def first(self, *, ignore_nulls: bool = False) -> DataFrame:
        """
        Aggregate the first values in the group.

        Parameters
        ----------
        ignore_nulls
            Ignore null values (default `False`).
            If set to `True`, the first non-null value for each aggregation is returned,
            otherwise `None` is returned if no non-null value exists.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [None, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).first()
        shape: (3, 4)
        ┌────────┬─────┬──────┬───────┐
        │ d      ┆ a   ┆ b    ┆ c     │
        │ ---    ┆ --- ┆ ---  ┆ ---   │
        │ str    ┆ i64 ┆ f64  ┆ bool  │
        ╞════════╪═════╪══════╪═══════╡
        │ Apple  ┆ 1   ┆ 0.5  ┆ null  │
        │ Orange ┆ 2   ┆ 0.5  ┆ true  │
        │ Banana ┆ 4   ┆ 13.0 ┆ false │
        └────────┴─────┴──────┴───────┘
        >>> df.group_by("d", maintain_order=True).first(ignore_nulls=True)
        shape: (3, 4)
        ┌────────┬─────┬──────┬───────┐
        │ d      ┆ a   ┆ b    ┆ c     │
        │ ---    ┆ --- ┆ ---  ┆ ---   │
        │ str    ┆ i64 ┆ f64  ┆ bool  │
        ╞════════╪═════╪══════╪═══════╡
        │ Apple  ┆ 1   ┆ 0.5  ┆ true  │
        │ Orange ┆ 2   ┆ 0.5  ┆ true  │
        │ Banana ┆ 4   ┆ 13.0 ┆ false │
        └────────┴─────┴──────┴───────┘
        """
        return self.agg(F.all().first(ignore_nulls=ignore_nulls))

    def last(self, *, ignore_nulls: bool = False) -> DataFrame:
        """
        Aggregate the last values in the group.

        Parameters
        ----------
        ignore_nulls
            Ignore null values (default `False`).
            If set to `True`, the last non-null value for each column is returned,
            otherwise `None` is returned if no non-null value exists.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 14, None],
        ...         "c": [True, True, True, None, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).last()
        shape: (3, 4)
        ┌────────┬─────┬──────┬──────┐
        │ d      ┆ a   ┆ b    ┆ c    │
        │ ---    ┆ --- ┆ ---  ┆ ---  │
        │ str    ┆ i64 ┆ f64  ┆ bool │
        ╞════════╪═════╪══════╪══════╡
        │ Apple  ┆ 3   ┆ 10.0 ┆ null │
        │ Orange ┆ 2   ┆ 0.5  ┆ true │
        │ Banana ┆ 5   ┆ null ┆ true │
        └────────┴─────┴──────┴──────┘
        >>> df.group_by("d", maintain_order=True).last(ignore_nulls=True)
        shape: (3, 4)
        ┌────────┬─────┬──────┬──────┐
        │ d      ┆ a   ┆ b    ┆ c    │
        │ ---    ┆ --- ┆ ---  ┆ ---  │
        │ str    ┆ i64 ┆ f64  ┆ bool │
        ╞════════╪═════╪══════╪══════╡
        │ Apple  ┆ 3   ┆ 10.0 ┆ true │
        │ Orange ┆ 2   ┆ 0.5  ┆ true │
        │ Banana ┆ 5   ┆ 14.0 ┆ true │
        └────────┴─────┴──────┴──────┘
        """
        return self.agg(F.all().last(ignore_nulls=ignore_nulls))

    def max(self) -> DataFrame:
        """
        Reduce the groups to the maximal value.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).max()
        shape: (3, 4)
        ┌────────┬─────┬──────┬──────┐
        │ d      ┆ a   ┆ b    ┆ c    │
        │ ---    ┆ --- ┆ ---  ┆ ---  │
        │ str    ┆ i64 ┆ f64  ┆ bool │
        ╞════════╪═════╪══════╪══════╡
        │ Apple  ┆ 3   ┆ 10.0 ┆ true │
        │ Orange ┆ 2   ┆ 0.5  ┆ true │
        │ Banana ┆ 5   ┆ 14.0 ┆ true │
        └────────┴─────┴──────┴──────┘
        """
        return self.agg(F.all().max())

    def mean(self) -> DataFrame:
        """
        Reduce the groups to the mean values.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).mean()
        shape: (3, 4)
        ┌────────┬─────┬──────────┬──────────┐
        │ d      ┆ a   ┆ b        ┆ c        │
        │ ---    ┆ --- ┆ ---      ┆ ---      │
        │ str    ┆ f64 ┆ f64      ┆ f64      │
        ╞════════╪═════╪══════════╪══════════╡
        │ Apple  ┆ 2.0 ┆ 4.833333 ┆ 0.666667 │
        │ Orange ┆ 2.0 ┆ 0.5      ┆ 1.0      │
        │ Banana ┆ 4.5 ┆ 13.5     ┆ 0.5      │
        └────────┴─────┴──────────┴──────────┘
        """
        return self.agg(F.all().mean())

    def median(self) -> DataFrame:
        """
        Return the median per group.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "d": ["Apple", "Banana", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).median()
        shape: (2, 3)
        ┌────────┬─────┬──────┐
        │ d      ┆ a   ┆ b    │
        │ ---    ┆ --- ┆ ---  │
        │ str    ┆ f64 ┆ f64  │
        ╞════════╪═════╪══════╡
        │ Apple  ┆ 2.0 ┆ 4.0  │
        │ Banana ┆ 4.0 ┆ 13.0 │
        └────────┴─────┴──────┘
        """
        return self.agg(F.all().median())

    def min(self) -> DataFrame:
        """
        Reduce the groups to the minimal value.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).min()
        shape: (3, 4)
        ┌────────┬─────┬──────┬───────┐
        │ d      ┆ a   ┆ b    ┆ c     │
        │ ---    ┆ --- ┆ ---  ┆ ---   │
        │ str    ┆ i64 ┆ f64  ┆ bool  │
        ╞════════╪═════╪══════╪═══════╡
        │ Apple  ┆ 1   ┆ 0.5  ┆ false │
        │ Orange ┆ 2   ┆ 0.5  ┆ true  │
        │ Banana ┆ 4   ┆ 13.0 ┆ false │
        └────────┴─────┴──────┴───────┘
        """
        return self.agg(F.all().min())

    def n_unique(self) -> DataFrame:
        """
        Count the unique values per group.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 1, 3, 4, 5],
        ...         "b": [0.5, 0.5, 0.5, 10, 13, 14],
        ...         "d": ["Apple", "Banana", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).n_unique()
        shape: (2, 3)
        ┌────────┬─────┬─────┐
        │ d      ┆ a   ┆ b   │
        │ ---    ┆ --- ┆ --- │
        │ str    ┆ u32 ┆ u32 │
        ╞════════╪═════╪═════╡
        │ Apple  ┆ 2   ┆ 2   │
        │ Banana ┆ 3   ┆ 3   │
        └────────┴─────┴─────┘
        """
        return self.agg(F.all().n_unique())

    def quantile(
        self, quantile: float, interpolation: QuantileMethod = "nearest"
    ) -> DataFrame:
        """
        Compute the quantile per group.

        Parameters
        ----------
        quantile
            Quantile between 0.0 and 1.0.
        interpolation : {'nearest', 'higher', 'lower', 'midpoint', 'linear', 'equiprobable'}
            Interpolation method.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).quantile(1)
        shape: (3, 3)
        ┌────────┬─────┬──────┐
        │ d      ┆ a   ┆ b    │
        │ ---    ┆ --- ┆ ---  │
        │ str    ┆ f64 ┆ f64  │
        ╞════════╪═════╪══════╡
        │ Apple  ┆ 3.0 ┆ 10.0 │
        │ Orange ┆ 2.0 ┆ 0.5  │
        │ Banana ┆ 5.0 ┆ 14.0 │
        └────────┴─────┴──────┘
        """  # noqa: W505
        return self.agg(F.all().quantile(quantile, interpolation=interpolation))

    def sum(self) -> DataFrame:
        """
        Reduce the groups to the sum.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).sum()
        shape: (3, 4)
        ┌────────┬─────┬──────┬─────┐
        │ d      ┆ a   ┆ b    ┆ c   │
        │ ---    ┆ --- ┆ ---  ┆ --- │
        │ str    ┆ i64 ┆ f64  ┆ u32 │
        ╞════════╪═════╪══════╪═════╡
        │ Apple  ┆ 6   ┆ 14.5 ┆ 2   │
        │ Orange ┆ 2   ┆ 0.5  ┆ 1   │
        │ Banana ┆ 9   ┆ 27.0 ┆ 1   │
        └────────┴─────┴──────┴─────┘
        """
        return self.agg(F.all().sum())


class RollingGroupBy:
    """
    A rolling grouper.

    This has an `.agg` method which will allow you to run all polars expressions in a
    group by context.
    """

    def __init__(
        self,
        df: DataFrame,
        index_column: IntoExpr,
        *,
        period: str | timedelta,
        offset: str | timedelta | None,
        closed: ClosedInterval,
        group_by: IntoExpr | Iterable[IntoExpr] | None,
        predicates: Iterable[Any] | None,
    ) -> None:
        period = parse_as_duration_string(period)
        offset = parse_as_duration_string(offset)

        self.df = df
        self.time_column = index_column
        self.period = period
        self.offset = offset
        self.closed = closed
        self.group_by = group_by
        self.predicates = predicates

    def __iter__(self) -> Self:
        from polars.lazyframe.opt_flags import QueryOptFlags

        temp_col = "__POLARS_GB_GROUP_INDICES"
        groups_df = (
            self.df.lazy()
            .with_row_index("__POLARS_GB_ROW_INDEX")
            .rolling(
                index_column=self.time_column,
                period=self.period,
                offset=self.offset,
                closed=self.closed,
                group_by=self.group_by,
            )
            .agg(F.first().alias(temp_col))
            .collect(optimizations=QueryOptFlags.none())
        )

        self._group_names = groups_df.select(F.all().exclude(temp_col)).iter_rows()
        self._group_indices = groups_df.select(temp_col).to_series()
        self._current_index = 0

        return self

    def __next__(self) -> tuple[tuple[object, ...], DataFrame]:
        if self._current_index >= len(self._group_indices):
            raise StopIteration

        group_name = next(self._group_names)
        group_data = self.df[self._group_indices[self._current_index], :]
        self._current_index += 1

        return group_name, group_data

    def having(self, *predicates: IntoExpr | Iterable[IntoExpr]) -> RollingGroupBy:
        """
        Filter groups with a list of predicates after aggregation.

        Using this method is equivalent to adding the predicates to the aggregation and
        filtering afterwards.

        This method can be chained and all conditions will be combined using `&`.

        Parameters
        ----------
        *predicates
            Expressions that evaluate to a boolean value for each group. Typically, this
            requires the use of an aggregation function. Multiple predicates are
            combined using `&`.
        """
        return RollingGroupBy(
            self.df,
            self.time_column,
            period=self.period,
            offset=self.offset,
            closed=self.closed,
            group_by=self.group_by,
            predicates=_chain_predicates(self.predicates, predicates),
        )

    def agg(
        self,
        *aggs: IntoExpr | Iterable[IntoExpr],
        **named_aggs: IntoExpr,
    ) -> DataFrame:
        """
        Compute aggregations for each group of a group by operation.

        Parameters
        ----------
        *aggs
            Aggregations to compute for each group of the group by operation,
            specified as positional arguments.
            Accepts expression input. Strings are parsed as column names.
        **named_aggs
            Additional aggregations, specified as keyword arguments.
            The resulting columns will be renamed to the keyword used.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        group_by = self.df.lazy().rolling(
            index_column=self.time_column,
            period=self.period,
            offset=self.offset,
            closed=self.closed,
            group_by=self.group_by,
        )
        if self.predicates:
            group_by = group_by.having(self.predicates)

        return group_by.agg(*aggs, **named_aggs).collect(
            optimizations=QueryOptFlags.none()
        )

    def map_groups(
        self,
        function: Callable[[DataFrame], DataFrame],
        schema: SchemaDict | None,
    ) -> DataFrame:
        """
        Apply a custom/user-defined function (UDF) over the groups as a new DataFrame.

        Using this is considered an anti-pattern as it will be very slow because:

        - it forces the engine to materialize the whole `DataFrames` for the groups.
        - it is not parallelized.
        - it blocks optimizations as the passed python function is opaque to the
          optimizer.

        The idiomatic way to apply custom functions over multiple columns is using:

        `pl.struct([my_columns]).map_elements(lambda struct_series: ..)`

        Parameters
        ----------
        function
            Function to apply over each group of the `LazyFrame`; it receives
            a DataFrame and should return a DataFrame.
        schema
            Schema of the output function. This has to be known statically. If the
            given schema is incorrect, this is a bug in the caller's query and may
            lead to errors. If set to None, polars assumes the schema is unchanged.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        if self.predicates:
            msg = "cannot call `map_groups` when filtering groups with `having`"
            raise TypeError(msg)

        return (
            self.df.lazy()
            .rolling(
                index_column=self.time_column,
                period=self.period,
                offset=self.offset,
                closed=self.closed,
                group_by=self.group_by,
            )
            .map_groups(function, schema)
            .collect(optimizations=QueryOptFlags.none())
        )


class DynamicGroupBy:
    """
    A dynamic grouper.

    This has an `.agg` method which allows you to run all polars expressions in a
    group by context.
    """

    def __init__(
        self,
        df: DataFrame,
        index_column: IntoExpr,
        *,
        every: str | timedelta,
        period: str | timedelta | None,
        offset: str | timedelta | None,
        include_boundaries: bool,
        closed: ClosedInterval,
        label: Label,
        group_by: IntoExpr | Iterable[IntoExpr] | None,
        start_by: StartBy,
        predicates: Iterable[Any] | None,
    ) -> None:
        every = parse_as_duration_string(every)
        period = parse_as_duration_string(period)
        offset = parse_as_duration_string(offset)

        self.df = df
        self.time_column = index_column
        self.every = every
        self.period = period
        self.offset = offset
        self.label = label
        self.include_boundaries = include_boundaries
        self.closed = closed
        self.group_by = group_by
        self.start_by = start_by
        self.predicates = predicates

    def __iter__(self) -> Self:
        from polars.lazyframe.opt_flags import QueryOptFlags

        temp_col = "__POLARS_GB_GROUP_INDICES"
        groups_df = (
            self.df.lazy()
            .with_row_index("__POLARS_GB_ROW_INDEX")
            .group_by_dynamic(
                index_column=self.time_column,
                every=self.every,
                period=self.period,
                offset=self.offset,
                label=self.label,
                include_boundaries=self.include_boundaries,
                closed=self.closed,
                group_by=self.group_by,
                start_by=self.start_by,
            )
            .agg(F.first().alias(temp_col))
            .collect(optimizations=QueryOptFlags.none())
        )

        self._group_names = groups_df.select(F.all().exclude(temp_col)).iter_rows()
        self._group_indices = groups_df.select(temp_col).to_series()
        self._current_index = 0

        return self

    def __next__(self) -> tuple[tuple[object, ...], DataFrame]:
        if self._current_index >= len(self._group_indices):
            raise StopIteration

        group_name = next(self._group_names)
        group_data = self.df[self._group_indices[self._current_index], :]
        self._current_index += 1

        return group_name, group_data

    def having(self, *predicates: IntoExpr | Iterable[IntoExpr]) -> DynamicGroupBy:
        """
        Filter groups with a list of predicates after aggregation.

        Using this method is equivalent to adding the predicates to the aggregation and
        filtering afterwards.

        This method can be chained and all conditions will be combined using `&`.

        Parameters
        ----------
        *predicates
            Expressions that evaluate to a boolean value for each group. Typically, this
            requires the use of an aggregation function. Multiple predicates are
            combined using `&`.
        """
        return DynamicGroupBy(
            self.df,
            self.time_column,
            every=self.every,
            period=self.period,
            offset=self.offset,
            include_boundaries=self.include_boundaries,
            closed=self.closed,
            label=self.label,
            group_by=self.group_by,
            start_by=self.start_by,
            predicates=_chain_predicates(self.predicates, predicates),
        )

    def agg(
        self,
        *aggs: IntoExpr | Iterable[IntoExpr],
        **named_aggs: IntoExpr,
    ) -> DataFrame:
        """
        Compute aggregations for each group of a group by operation.

        Parameters
        ----------
        *aggs
            Aggregations to compute for each group of the group by operation,
            specified as positional arguments.
            Accepts expression input. Strings are parsed as column names.
        **named_aggs
            Additional aggregations, specified as keyword arguments.
            The resulting columns will be renamed to the keyword used.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        group_by = self.df.lazy().group_by_dynamic(
            index_column=self.time_column,
            every=self.every,
            period=self.period,
            offset=self.offset,
            label=self.label,
            include_boundaries=self.include_boundaries,
            closed=self.closed,
            group_by=self.group_by,
            start_by=self.start_by,
        )
        if self.predicates:
            group_by = group_by.having(self.predicates)

        return group_by.agg(*aggs, **named_aggs).collect(
            optimizations=QueryOptFlags.none()
        )

    def map_groups(
        self,
        function: Callable[[DataFrame], DataFrame],
        schema: SchemaDict | None,
    ) -> DataFrame:
        """
        Apply a custom/user-defined function (UDF) over the groups as a new DataFrame.

        Using this is considered an anti-pattern as it will be very slow because:

        - it forces the engine to materialize the whole `DataFrames` for the groups.
        - it is not parallelized.
        - it blocks optimizations as the passed python function is opaque to the
          optimizer.

        The idiomatic way to apply custom functions over multiple columns is using:

        `pl.struct([my_columns]).map_elements(lambda struct_series: ..)`

        Parameters
        ----------
        function
            Function to apply over each group of the `LazyFrame`; it receives
            a DataFrame and should return a DataFrame.
        schema
            Schema of the output function. This has to be known statically. If the
            given schema is incorrect, this is a bug in the caller's query and may
            lead to errors. If set to None, polars assumes the schema is unchanged.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        if self.predicates:
            msg = "cannot call `map_groups` when filtering groups with `having`"
            raise TypeError(msg)

        return (
            self.df.lazy()
            .group_by_dynamic(
                index_column=self.time_column,
                every=self.every,
                period=self.period,
                offset=self.offset,
                include_boundaries=self.include_boundaries,
                closed=self.closed,
                group_by=self.group_by,
                start_by=self.start_by,
            )
            .map_groups(function, schema)
            .collect(optimizations=QueryOptFlags.none())
        )


def _chain_predicates(
    lhs: Iterable[IntoExpr] | None, rhs: tuple[IntoExpr | Iterable[IntoExpr], ...]
) -> Iterable[Any]:
    return (
        chain(lhs, _parse_inputs_as_iterable(rhs))
        if lhs is not None
        else _parse_inputs_as_iterable(rhs)
    )
